Linear regression

main.py

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+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+names = ['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity']
+x_names = ['mileage', 'year', 'brand', 'engineType', 'engineCapacity']
+names_without_brand = ['mileage', 'year', 'engineType', 'engineCapacity']
+
+
+def main():
+    train_x, train_y = get_train_data()
+
+    model = LinearRegression()
+    model.fit(train_x, train_y)
+
+    dev_x, dev_y = get_dev_data()
+
+    predicted_dev_y = model.predict(dev_x)
+    save_csv(predicted_dev_y, 'dev-0/out.tsv')
+
+    test_x = get_test_data()
+
+    predicted_test_y = model.predict(test_x)
+    save_csv(predicted_test_y, 'test-A/out.tsv')
+
+    print(RMSE(dev_y, predicted_dev_y))
+
+
+def get_train_data():
+    raw_data = pd.read_csv('train/train.tsv', sep='\t', names=names)
+    x = raw_data[names_without_brand]
+    x = pd.get_dummies(x, columns=['engineType'])
+    y = raw_data['price']
+
+    return x, y
+
+
+def get_dev_data():
+    dev_raw_data = pd.read_csv('dev-0/in.tsv', sep='\t', names=x_names)
+    x = dev_raw_data[names_without_brand]
+    x = pd.get_dummies(x, columns=['engineType'])
+
+    with open('dev-0/expected.tsv', 'r') as file:
+        y = [float(line.strip('\n')) for line in file.readlines()]
+
+    return x, y
+
+
+def get_test_data():
+    test_raw_data = pd.read_csv('test-A/in.tsv', sep='\t', names=x_names)
+    x = test_raw_data[names_without_brand]
+    x = pd.get_dummies(x, columns=['engineType'])
+
+    return x
+
+
+def save_csv(data, path):
+    df = pd.DataFrame(data)
+    df.to_csv(path, sep='\t', index=False, header=False)
+
+
+def RMSE(dev_y, predicted_dev_y):
+    return np.sqrt(mean_squared_error(dev_y, predicted_dev_y))
+
+
+if __name__ == '__main__':
+    main()